Autonomous vehicles such as, for example, unmanned aerial vehicles, typically include one or more calibrated sensors that are mounted (damped or directly affixed) to the vehicle frame. The sensors included on autonomous vehicles are combined to construct a multi-spectral sensing payload (e.g. LIDAR, SONAR, EO cameras, etc.) that is used in conjunction with real-time sensor fusion and perception algorithms to extract features, objects and/or regions within the operating environment of the vehicle. Unfortunately, the utility of the vehicle's sensing payload for closing the loop in this manner is conditioned upon an underlying assumption that the extrinsic calibration parameters (i.e., the value parameters capturing the relative pose of the sensor with respect to a predefined vehicle body frame) remain as reliable approximations of the true sensor pose between calibrations. This assumption does not always hold true, especially when one considers potential sensor perturbations. Under such circumstances, the sensor may (depending upon the severity of the perturbation) continue to report reasonable sensor measurements in its local frame (i.e., the sensor itself is operating as expected). These measurements, however, will not be properly integrated into the vehicle's world model as the underlying geometric model has fundamentally changed. Furthermore, incorporating the measured output of one or more uncalibrated sensors creates an inaccurate world model.